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NEW QUESTION # 55
You are an infrastructure engineer tasked with validating a new AI training cluster before releasing it to users.
Your team wants to perform a NeMo burn-in to ensure both hardware and software are reliable and ready for production workloads. Which of the following actions are required as part of a proper NeMo burn-in process?
Pick the 2 correct responses below.
Answer: A,D
Explanation:
A proper NeMo burn-in should use a representative training or pretraining workload and run it for a sustained period across the intended GPUs and nodes. The goal is not only to prove that NeMo can import or that a model can return an inference result. The goal is to exercise the full AI infrastructure path: GPUs, CPU memory, CUDA, NCCL, network fabric, storage, container runtime, scheduler integration, and framework stack. Configuring a representative NeMo recipe and executor ensures the job launches with realistic distributed-training behavior and uses the same type of resources expected in production. Running the job repeatedly or for an extended duration helps expose intermittent issues such as NCCL stalls, GPU Xid errors, storage slowdowns, thermal throttling, node instability, or performance regression. A quick accuracy check on a pre-trained model is not a burn-in because it does not adequately stress distributed training. Inference-only testing is also insufficient for validating a training cluster. NeMo burn-in is valuable because it validates both the software framework and the underlying NVIDIA AI infrastructure under realistic workload pressure.
NEW QUESTION # 56
Which of the following is the MOST important reason for using a dedicated storage network (e.g., InfiniBand or RoCE) for AI/ML workloads compared to using the existing Ethernet network?
Answer: D
Explanation:
The primary benefit of a dedicated storage network like InfiniBand or RoCE is the significant improvement in latency and bandwidth (option B) compared to Ethernet. These technologies are designed for high-performance computing and can handle the intense 1/0 demands of AI/ML workloads. While security (A) can be improved through isolation, and QOS (E) is possible, the performance advantage is the most crucial factor. Cost (D) is generally higher, and management (C) can be more complex.
NEW QUESTION # 57
During a 48-hour NeMo question-answering model burn-in test, GPU memory errors occur when processing large datasets. Which configuration strategy prevents Out-of-Memory (OOM) errors while maintaining processing efficiency?
Answer: D
Explanation:
NVIDIA NeMo and large language model (LLM) training workloads are extremely demanding onHBM (High Bandwidth Memory). Out-of-Memory (OOM) errors often occur not because the total dataset is too large, but because memory fragmentation or sudden spikes in allocation (spikes during data shuffling or batch loading) exceed the available GPU memory. To mitigate this during intensive burn-in tests, engineers utilize theRMM (RAPIDS Memory Manager)library, which provides an asynchronous allocator. Enabling RMM asynchronous allocation allows the system to pre-allocate a pool of memory and manage it more efficiently than the standard CUDA allocator, reducing the overhead of constant allocations and deallocations.
Furthermore, setting a specific blocksize (e.g., 1GB) for data loading ensures that the data ingestion pipeline reads data in manageable, deterministic chunks. This prevents the system from attempting to load massive files entirely into memory at once, which is the primary cause of OOMs in question-answering tasks involving large Parquet or JSON datasets. Switching to FP32 (Option B) would actually double the memory footprint and increase the likelihood of an OOM error.
NEW QUESTION # 58
You suspect a faulty NVIDIA ConnectX-6 network adapter in a server used for RDMA-based distributed training. Which commands or tools can you use to diagnose potential issues with the adapter's hardware and connectivity?
Answer: A,C,D,E
Explanation:
All options except E are relevant for diagnosing network adapter issues. 'Ispci -v' (A) verifies hardware detection. 'ibstat' (B) checks InfiniBand-specific details. 'ethtoor (C) examines Ethernet settings. 'ping' (D) tests basic connectivity. 'nvsmimonitord' (E) focuses on GPU monitoring, not network adapters.
NEW QUESTION # 59
You're deploying a BlueField-2 DPU in a cloud environment and need to ensure the integrity of the DPU's firmware. You want to verify that the firmware hasn't been tampered with. Which of the following methods provides the strongest level of assurance for firmware integrity?
Answer: D
Explanation:
Digitally signed firmware provides the strongest guarantee of integrity. The signature verifies that the firmware hasn't been tampered with since it was signed by NVIDIA. SHA256 checksums are good, but digital signatures are cryptographically stronger. MD5 checksums are considered weak and easily compromised. Firmware version and file size offer minimal assurance against sophisticated attacks.
NEW QUESTION # 60
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